CN116776642A - Method and device for creating solar radiation short-term forecast model based on aerosol and cloud - Google Patents
Method and device for creating solar radiation short-term forecast model based on aerosol and cloud Download PDFInfo
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Abstract
The embodiment of the disclosure provides a solar radiation short-term forecast model creation method and device based on aerosol and cloud, and relates to the technical field of weather forecast, wherein the model creation method comprises the following steps: starting a WRF-Solar and a WRF-Chem numerical mode to obtain a WRF-Chem-Solar online coupling mode; establishing a pollution source list by adopting pollution source data comprising local atmospheric pollution emission source data and biological source emission data; adopting a GSI assimilation system to assimilate the WRF-Chem-Solar online coupling mode data after the pollution source list is created; evaluating the WRF-Chem-Solar online coupling mode after assimilation; and when the evaluation result meets the preset requirement, taking the WRF-Chem-Solar online coupling mode for storing the assimilation treatment parameters as a Solar radiation short-term forecasting model based on aerosol and cloud. The embodiment of the disclosure is beneficial to improving the accuracy of solar radiation forecasting.
Description
Technical Field
The disclosure relates to the technical field of weather forecast, in particular to a solar radiation short-term forecast model creation method and device based on aerosol and cloud.
Background
Cloud is the most dominant factor affecting ground solar radiation, and in addition, solar radiation is also affected by aerosols, atmospheric trace gases, and the like. WRF (The Weather Research and Forecasting Model, weather forecast) mode is a currently common dynamic downscaling solar radiation physical forecast mode. The WRF-Solar (Solar radiation forecast) numerical mode is a numerical weather forecast model specially designed for Solar resource assessment and forecast requirements, fully considers a feedback mechanism among cloud, aerosol and Solar radiation, and can independently output total radiation, direct radiation and indirect radiation of each time step according to requirements. The WRF-Chem (weather chemistry forecast) numerical mode is an online aerodynamic chemical mode, the complete coupling of weather and chemical modes on space-time resolution is realized, the mode considers the chemical process of atmospheric pollution, advection, turbulence diffusion and dry-wet sedimentation processes, and trace gas, aerosol concentration and weather field can be simulated simultaneously.
Disclosure of Invention
The method and the device for creating the solar radiation short-term forecasting model based on the aerosol and the cloud are beneficial to improving the accuracy of solar radiation forecasting.
In a first aspect, embodiments of the present disclosure provide a method for creating a short-term solar radiation prediction model based on aerosol and cloud, the method comprising:
starting a Solar radiation forecast WRF-Solar numerical mode and a weather chemistry forecast WRF-Chem numerical mode to obtain a weather chemistry Solar radiation forecast WRF-Chem-Solar online coupling mode;
establishing a pollution source list of the WRF-Chem-Solar online coupling mode by adopting pollution source data comprising local atmospheric pollution emission source data and biological source emission data;
adopting a preset grid point statistical difference GSI assimilation system to assimilate data of the WRF-Chem-Solar online coupling mode after the pollution source list is created; the assimilation treatment comprises: weather data assimilation, aerosol data assimilation, and cloud data assimilation;
evaluating the WRF-Chem-Solar online coupling mode after data assimilation treatment;
and under the condition that the evaluation result meets the preset requirement, taking the WRF-Chem-Solar online coupling mode after the assimilation treatment parameters are stored as a Solar radiation short-term forecasting model based on aerosol and cloud.
In a second aspect, embodiments of the present disclosure provide a solar radiation forecasting method, the method comprising:
acquiring an aerosol and cloud-based solar radiation short-term prediction model created according to the aerosol and cloud-based solar radiation short-term prediction model creation method;
periodically carrying out assimilation treatment on the solar radiation short-term forecasting model based on aerosol and cloud according to observation data comprising meteorological data, aerosol data and cloud data;
and forecasting solar radiation by adopting an assimilation-treated solar radiation short-term forecasting model based on aerosol and cloud.
In a third aspect, embodiments of the present disclosure provide an aerosol and cloud-based solar radiation short-term forecast model creation apparatus, including:
the starting module is used for starting a Solar radiation forecast WRF-Solar numerical mode and a weather chemistry forecast WRF-Chem numerical mode to obtain a weather chemistry Solar radiation forecast WRF-Chem-Solar online coupling mode;
the establishing module is used for establishing a pollution source list of the WRF-Chem-Solar online coupling mode by adopting pollution source data comprising local atmospheric pollution emission source data and biological source emission data;
the first assimilation module is used for carrying out data assimilation treatment on the WRF-Chem-Solar online coupling mode after the pollution source list is created by adopting a preset grid point statistical difference GSI assimilation system; the assimilation treatment comprises: weather data assimilation, aerosol data assimilation, and cloud data assimilation;
the evaluation module is used for evaluating the WRF-Chem-Solar online coupling mode after the data assimilation treatment;
and the processing module is used for taking the WRF-Chem-Solar on-line coupling mode after the assimilation processing parameter is stored as a Solar radiation short-term forecasting model based on aerosol and cloud under the condition that the evaluation result meets the preset requirement.
In a fourth aspect, embodiments of the present disclosure provide a solar radiation forecasting apparatus, comprising:
the acquisition module is used for acquiring the aerosol and cloud-based solar radiation short-term prediction model created according to the aerosol and cloud-based solar radiation short-term prediction model creation method;
the second assimilation module is used for carrying out periodic assimilation treatment on the solar radiation short-term forecast model based on the aerosol and the cloud according to observation data comprising meteorological data, aerosol data and cloud data;
and the forecasting module is used for forecasting solar radiation by adopting an assimilation-processed solar radiation short-term forecasting model based on aerosol and cloud.
In a fifth aspect, embodiments of the present disclosure provide an electronic device, including:
one or more processors;
a memory having stored thereon one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the aerosol and cloud based short-term solar radiation prediction model creation method and/or the solar radiation prediction method;
one or more input/output I/O interfaces coupled between the processor and the memory configured to enable information interaction of the processor with the memory.
In a sixth aspect, embodiments of the present disclosure provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the aerosol and cloud based solar radiation short-term forecast model creation method and/or the solar radiation forecast method.
According to the embodiment of the disclosure, by establishing the WRF-Chem-Solar online coupling mode, the respective simulation characteristics and advantages of the WRF-Solar mode and the WRF-Chem mode can be respectively drawn; pollution source data comprising local atmospheric pollution emission source data and biological source emission data are introduced, live observation data such as meteorological data, aerosol data and cloud data are assimilated, the influence of the pollution source on the atmospheric aerosol is realized, and the real-time online consideration of the aerosol and cloud change on the solar radiation effect is realized, so that the solar radiation prediction accuracy is improved.
Drawings
In the drawings of the embodiments of the present disclosure:
FIG. 1 is a flowchart of a method of creating an aerosol and cloud based short-term solar radiation forecast model in accordance with an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a method of creating a short-term solar radiation forecast model based on aerosols and clouds in accordance with an embodiment of the disclosure;
FIG. 3 is a flowchart of a solar radiation forecasting method in accordance with an embodiment of the present disclosure;
FIG. 4 is a block diagram of an aerosol and cloud based short-term solar radiation prediction model creation device according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of a solar radiation forecasting device in accordance with an embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
In order to better understand the technical solutions of the present disclosure, the following describes in detail a communication-aware data processing method and a computer-readable storage medium provided by embodiments of the present disclosure with reference to the accompanying drawings.
The present disclosure will be described more fully hereinafter with reference to the accompanying drawings, but the embodiments shown may be embodied in different forms and should not be construed as limited to the embodiments set forth below. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The accompanying drawings, which are included to provide a further understanding of embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the detailed embodiment, do not limit the disclosure. The above and other features and advantages will become more readily apparent to those skilled in the art from the description of the detailed embodiments with reference to the accompanying drawings.
The present disclosure may be described with reference to plan and/or cross-sectional views with the aid of idealized schematic diagrams of the present disclosure. Accordingly, the example illustrations may be modified in accordance with manufacturing techniques and/or tolerances.
Embodiments of the disclosure and features of embodiments may be combined with each other without conflict.
The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The term "and/or" as used in this disclosure includes any and all combinations of one or more of the associated listed items. As used in this disclosure, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," "includes," "including," "having," "including," "made of … …" and/or "comprising," when used in this disclosure, specify the presence of stated features, integers, steps, operations, elements, and/or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
Unless otherwise defined, all terms (including technical and scientific terms) used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The present disclosure is not limited to the embodiments shown in the drawings, but includes modifications of the configuration formed based on the manufacturing process. Thus, the regions illustrated in the figures have schematic properties and the shapes of the regions illustrated in the figures illustrate the particular shapes of the regions of the elements, but are not intended to be limiting.
Cloud is the most dominant factor affecting ground solar radiation, and in addition, solar radiation is also affected by aerosols, atmospheric trace gases, and the like. WRF (The Weather Research and Forecasting Model, weather forecast) mode is a currently common dynamic downscaling solar radiation physical forecast mode. The WRF-Solar (Solar radiation forecast) numerical mode is a numerical weather forecast model specially designed for Solar resource assessment and forecast requirements, fully considers a feedback mechanism among cloud, aerosol and Solar radiation, and can independently output total radiation, direct radiation and indirect radiation of each time step according to requirements. The WRF-Chem (weather chemistry forecast) numerical mode is an online aerodynamic chemical mode, the complete coupling of weather and chemical modes on space-time resolution is realized, the mode considers the chemical process of atmospheric pollution, advection, turbulence diffusion and dry-wet sedimentation processes, and trace gas, aerosol concentration and weather field can be simulated simultaneously.
The WRF-Solar numerical mode has no influence on atmospheric aerosol, trace gas and the like due to lack of real-time change of aerosol and cloud and on-line consideration of Solar radiation absorption and scattering effects, and emission of pollution sources such as artificial sources, biological sources and the like, so that the forecasting effect on Solar radiation is influenced. The WRF-Chem numerical mode lacks sufficient consideration of a cloud-aerosol-radiation process, and cannot meet the output requirement of photovoltaic power generation on a solar radiation forecast result. In addition, in the solar radiation forecasting numerical mode, the forecasting precision is improved by assimilating aerosol optical thickness (AOD) data, but the forecasting precision is improved by assimilating FY (wind cloud meteorological satellite) satellite cloud inversion products.
Aiming at the defects of the current scheme and the requirements of actual Solar radiation prediction, the scheme of the embodiment of the disclosure provides a method for coupling a WRF-Chem aerosol chemical mode with a WRF-Solar radiation mode, establishes a WRF-Chem-Solar coupling online mode, establishes a Solar radiation short-term prediction model based on aerosol and cloud, realizes real-time consideration of an atmospheric radiation process, adds a local pollution source list into the model, assimilates live observation data such as meteorological elements and aerosol, assimilates inversion products such as FY satellite cloud coverage rate, cloud top height and cloud top temperature, improves the initial conditions of the coupling mode, and achieves the aim of improving the Solar radiation prediction precision. Embodiments of the present disclosure are described in detail below.
The embodiment of the disclosure provides a solar radiation short-term forecast model creation method based on aerosol and cloud, as shown in fig. 1 and 2, the method may include steps S11-S15:
s11, starting a Solar radiation forecast WRF-Solar numerical mode and a weather chemistry forecast WRF-Chem numerical mode to obtain a weather chemistry Solar radiation forecast WRF-Chem-Solar online coupling mode.
In the embodiment of the disclosure, the WRF-Solar numerical mode and the WRF-Chem numerical mode can be started together to construct a WRF-Chem-Solar online coupling mode, online coupling of the chemical aerosol module and the radiation mode, and online consideration of the air aerosol radiation process is realized.
S12, a pollution source list of a WRF-Chem-Solar online coupling mode is established by adopting pollution source data comprising local air pollution emission source data and biological source emission data.
In an embodiment of the present disclosure, a pollution source list of WRF-Chem-Solar online coupling modes is established using pollution source data comprising local atmospheric pollution emission source data and biological source emission data, comprising:
obtaining a local emission source list according to the local atmospheric pollution emission source data;
taking output data of a preset period input by a global atmospheric air conveying mode MOZART as a chemical field;
acquiring an online gas and aerosol emission model list MEGAN list from the natural world as a biological source emission list;
the local emission source list, the chemical field emission list and the biological source emission list are used as pollution source list of the WRF-Chem-Solar online coupling mode.
In an embodiment of the present disclosure, obtaining a local emissions source list from local atmospheric pollution emissions source data includes:
updating a multi-scale emission list MEIC by using the local atmospheric pollution emission source data to obtain local emission source list data;
interpolating the local emissions source inventory data onto pattern grid points of the WRF-Chem-Solar online coupling pattern using a sparse matrix emissions inventory processing system SMOKE source pattern;
and obtaining the emission source intensity according to the local emission source list data, and carrying out gridding treatment on the emission source intensity to obtain a local emission source list.
In the embodiment of the disclosure, the MEIC (multi-resolution emission inventory for china, china multi-scale emission list) source list can be updated by using the local atmospheric pollution emission source data (i.e. artificial emission data) obtained by investigation, the local emission source list data is interpolated onto WRF-Chem-Solar online coupling mode grid points by using SMOKE (Sparse Matrix Operator Kernel Emissions, sparse matrix emission list processing system) source mode, and annual emission data is converted into space-time change gridded emission source intensity (the source intensity refers to the emission amount of pollutants in unit time) which is input into the WRF-Chem-Solar online coupling mode as the local emission source list.
In the disclosed embodiment, the chemical field may use MOZART (Model for Ozone and Related Chemical Tracers, global atmospheric air delivery mode) input data output every 6h (6 hours).
In the disclosed embodiments, the biogenic emissions inventory may employ an online MEGAN (The Model of Emissions of Gases and Aerosols from Nature, a list of gas and aerosol emissions models from nature) inventory.
S13, carrying out data assimilation treatment on the WRF-Chem-Solar online coupling mode after the pollution source list is created by adopting a preset grid point statistical difference GSI assimilation system; the assimilation treatment includes: weather data assimilation, aerosol data assimilation, and cloud data assimilation.
In embodiments of the present disclosure, the assimilation treatment may comprise, but is not limited to, at least one of the following: weather data assimilation, aerosol data assimilation, and cloud data assimilation. Each assimilation process will be described in detail below.
Adopting a preset grid point statistical difference GSI assimilation system to assimilate data of the WRF-Chem-Solar online coupling mode after creating the pollution source list, wherein the data assimilation process comprises at least one of the following steps:
assimilating meteorological observation data based on a GSI assimilation system to realize meteorological data assimilation;
based on Gorad chemical aerosol radiation and transmission scheme GOCART, the GSI assimilation system is utilized to assimilate the observation data of the preset inhalable particles, so as to realize aerosol data assimilation; the method comprises the steps of,
assimilating cloud data products of a preset satellite based on a GSI assimilation system to realize cloud data assimilation; cloud data products include, but are not limited to, at least one of: cloud coverage, cloud top temperature, and cloud top air pressure.
In the examples of the present disclosure, each of the assimilation treatments is described in detail below.
In the embodiment of the disclosure, the meteorological data (or data) is assimilated, and a GSI (Gridpoint Statistical Interpolation, grid point statistical difference) assimilation system can be utilized to assimilate conventional meteorological observation data, so that the initial meteorological conditions of the WRF-Chem-Solar online coupling mode are improved.
GSI assimilation systems were developed based on SSI (Spectral Statistical Interpolation, statistical interpolation of spectra) systems. The GSI assimilation system makes up for some defects of the SSI system in the method, and is a new generation assimilation system which can perform regional analysis and global analysis. GSI assimilation system is widely used in various meteorological modes, and can be well used in various modes.
The GSI assimilation system comprises: three-dimensional variation assimilation and mixed set variation assimilation. According to the embodiment of the disclosure, a three-dimensional variation assimilation method (3 DVAR) can be adopted, the 3DVAR mainly solves the minimum value of the objective function, so that the analysis field, the background field and the observation field achieve the best fitting effect, and the solution with the minimum preset cost function is the analysis field.
The 3DVAR method in the GSI assimilation system comprises the following operation steps: the method comprises the steps of converting conventional meteorological observation data into BUFR (Binary Universal Form for the Representation of meteorological data, binary common format representing the meteorological data) format data which can be identified by a GSI system, inputting a background field and an observation field into the GSI system, setting background error covariance (background error covariance refers to background field error covariance), observation error covariance (observation error covariance refers to observation data error covariance), assimilation time window and other parameterization settings, obtaining different assimilation results by different assimilation parameter settings, and iteratively solving a cost function until an optimal minimum solution is solved, so that a final analysis field is obtained.
In the presently disclosed embodiments, aerosol data assimilation, oriented to the goscart (Goddard Chemistry Aerosol Radiation and Transport, goldamde chemical aerosol radiation and delivery) aerosol protocol, utilizes a GSI assimilation system to assimilate conventional observational data for preset inhalable particulates (which may include, for example, but are not limited to, PM2.5 (particulates having an aerodynamic equivalent diameter of less than or equal to 2.5 microns), PM10 (particulates having an aerodynamic equivalent diameter of less than or equal to 10 microns)).
In an embodiment of the disclosure, based on a golddar chemical aerosol radiation and transmission scheme, goart, an aerosol data assimilation is realized by assimilating observation data of preset inhalable particulate matters by using a preset grid point statistical difference value, GSI, assimilation system, comprising:
encoding data of inhalable particles into data in a preset format;
adding data in a preset format into GDAS analysis data of a global data assimilation system;
and combining the added GDAS analysis data with ground station data to realize assimilation of the observation data of the preset inhalable particles.
In an embodiment of the present disclosure, combining the added GDAS analysis data with the ground station data includes: and performing at least one of the following processing on the added GDAS analysis data and the ground station data:
BUFR decoding, complement, encoding, overall detection, quality control, and bias correction.
In the embodiment of the disclosure, PM2.5 and PM10 regular observation data can be encoded into prepbufr (prepbufr is a data format for expanding code table content based on WMO (World Meteorological Organization, world weather organization) BUFR format by using NCEP (National Centers for Environmental Prediction, national environmental forecast center), and the data is supplemented based on analyzing data by using GDAS (Global Data Assimilation System ). In the process of combining GDAS data and ground station data, a series of processes including BUFR decoding (decoding), complementary coding (applying) and encoding (encoding) are added, and besides, the processes of general checking (gross checking), quality control (quality control), bias correction (bias correction) and the like are also included, so that a GSI assimilation system for assimilating ground observation data is improved.
In the embodiment of the disclosure, cloud data assimilation can be realized based on data products such as cloud coverage rate, cloud top temperature, cloud top air pressure and the like of a GSI assimilation system assimilation FY-4A (meteorological satellite) satellite, so that meteorological-aerosol-cloud combined data assimilation is realized, and initial conditions and boundary conditions of a WRF-Chem-Solar online coupling mode are improved.
In the embodiment of the present disclosure, the cloud data assimilation is similar to the conventional meteorological observation data assimilation manner described above, and will not be described herein.
S14, evaluating the WRF-Chem-Solar online coupling mode after data assimilation treatment.
In the embodiment of the disclosure, solar radiation prediction is performed by adopting a WRF-Chem-Solar online coupling mode after data assimilation treatment to obtain radiation prediction data and real radiation observation data; and evaluating the radiation forecast data and the radiation observation data based on evaluation indexes such as root mean square error, correlation coefficient, average error and the like so as to continuously optimize the combined assimilation parameter setting according to the evaluation result.
And S15, under the condition that the evaluation result meets the preset requirement, taking the WRF-Chem-Solar on-line coupling mode after the assimilation treatment parameters are stored as a Solar radiation short-term forecasting model based on aerosol and cloud.
In the embodiment of the present disclosure, the preset requirements may be defined according to requirements, for example, may include, but not limited to: the difference between the radiation forecast data and the radiation observation data is less than or equal to a preset difference threshold.
In the embodiment of the disclosure, under the condition that the evaluation result meets the preset requirement, the combined assimilation is demonstrated to reach the optimal parameter setting, the assimilation treatment parameter setting of the WRF-Chem-Solar online coupling mode can be saved, and the WRF-Chem-Solar online coupling mode is used as a Solar radiation short-term prediction model based on aerosol and cloud.
In an embodiment of the present disclosure, the method may further include: under the condition that the evaluation result does not meet the preset requirement, the assimilation treatment parameters of the WRF-Chem-Solar online coupling mode are adjusted;
evaluating the WRF-Chem-Solar online coupling mode after parameter adjustment;
the assimilation treatment parameters comprise at least one of the following: background error covariance, observation error covariance, and assimilation time window; the background error covariance refers to the background field error covariance; the observation error covariance refers to an observation data error covariance, and the observation data comprises at least one of the following: meteorological data, aerosol data, and cloud data.
In the embodiment of the disclosure, under the condition that the evaluation result does not meet the preset requirement, the assimilation treatment parameters can be continuously adjusted, assimilation is performed according to the adjusted parameters, the assimilation WRF-Chem-Solar online coupling mode is evaluated again until the evaluation result meets the preset requirement, and the WRF-Chem-Solar online coupling mode with the evaluation result meeting the preset requirement is used as a Solar radiation short-term prediction model based on aerosol and cloud.
In the embodiment of the disclosure, a WRF-Chem numerical mode and a WRF-Solar numerical mode are coupled, and a WRF-Chem-Solar online coupling mode is established, so that a Solar radiation short-term forecast model based on aerosol and cloud is obtained based on the WRF-Chem-Solar online coupling mode, high-precision information such as a local artificial emission source, a biological source and the like is introduced, and real-time aerosol observation data of an environment monitoring station are assimilated. Although the WRF-Solar numerical mode is a numerical weather forecast mode specifically designed for Solar resource assessment and forecast needs, the official release version lacks consideration of an online aerosol radiation process, so that the online WRF-Chem numerical mode and the WRF-Solar numerical mode are coupled, real-time aerosol observation data are assimilated, and cyclic assimilation of the aerosol data by the WRF-Chem numerical mode and the WRF-Solar numerical mode and consideration of the real-time online process of aerosol-radiation feedback action are realized.
In the embodiment of the disclosure, a Solar radiation short-term forecast model based on a WRF-Chem-Solar online coupling mode is established, respective simulation characteristics and advantages of the WRF-Solar numerical mode and the WRF-Chem numerical mode can be respectively drawn, a local pollution source list is introduced, live observation data such as meteorological elements, aerosol, cloud top information and cloud coverage rate are assimilated, and the influence of a pollution source on the atmospheric aerosol and real-time online consideration of the Solar radiation effect by aerosol and cloud change are realized.
In the embodiment of the disclosure, the cloud is the most important factor influencing solar radiation, and in a solar radiation short-term forecast model based on aerosol and cloud, information such as FY satellite cloud coverage rate, cloud top height, cloud phase and the like is assimilated, so that comprehensive real-time online consideration of an atmospheric aerosol-cloud-radiation process is realized.
The embodiment of the disclosure provides a solar radiation forecasting method, as shown in fig. 3, the method may include steps S21-S23:
s21, acquiring an aerosol and cloud-based solar radiation short-term prediction model created according to an aerosol and cloud-based solar radiation short-term prediction model creation method;
s22, periodically carrying out assimilation treatment on a solar radiation short-term forecasting model based on aerosol and cloud according to observation data comprising meteorological data, aerosol data and cloud data;
s23, forecasting solar radiation by adopting an aerosol and cloud-based solar radiation short-term forecasting model after assimilation treatment.
In the embodiment of the disclosure, the Solar radiation forecast can be developed by utilizing an aerosol and cloud-based Solar radiation short-term forecast model determined by a WRF-Chem-Solar online coupling mode after meteorological-aerosol-cloud joint data assimilation. Based on a parameter setting scheme of a solar radiation short-term forecasting model based on aerosol and cloud, the solar radiation short-term forecasting model can be circularly assimilated hour by hour, and short-term forecasting of solar radiation for 0-4 hours (4 hours) is carried out.
Embodiments of the present disclosure provide an aerosol and cloud-based solar radiation short-term forecast model creation apparatus 100, as shown in fig. 4, comprising:
the starting module 101 is used for starting a Solar radiation prediction WRF-Solar numerical mode and a weather chemistry prediction WRF-Chem numerical mode to obtain a weather chemistry Solar radiation prediction WRF-Chem-Solar online coupling mode;
the establishing module 102 is configured to establish a pollution source list of the WRF-Chem-Solar online coupling mode by using pollution source data including local atmospheric pollution emission source data and biological source emission data;
a first assimilation module 103, configured to perform data assimilation processing on the WRF-Chem-Solar online coupling mode after the pollution source list is created by using a preset grid point statistical difference GSI assimilation system; the assimilation treatment includes: weather data assimilation, aerosol data assimilation, and cloud data assimilation;
the evaluation module 104 is used for evaluating the WRF-Chem-Solar online coupling mode after the data assimilation treatment;
and the processing module 105 is used for taking the WRF-Chem-Solar on-line coupling mode after the assimilation processing parameter is stored as a Solar radiation short-term forecasting model based on aerosol and cloud under the condition that the evaluation result meets the preset requirement.
The disclosed embodiment provides a solar radiation forecasting apparatus 200, as shown in fig. 5, comprising:
an acquisition module 201, configured to acquire an aerosol-and cloud-based solar radiation short-term prediction model created according to an aerosol-and cloud-based solar radiation short-term prediction model creation method;
a second assimilation module 202 for performing periodic assimilation treatment on the aerosol-based and cloud-based solar radiation short-term forecast model according to observation data including meteorological data, aerosol data and cloud data;
and the forecasting module 203 is used for forecasting solar radiation by adopting an aerosol and cloud-based solar radiation short-term forecasting model after assimilation treatment.
An embodiment of the present disclosure provides an electronic device 300, as shown in fig. 6, the electronic device 300 includes:
one or more processors 301;
a memory 302 having one or more programs stored thereon, which when executed by the one or more processors 301, cause the one or more processors 301 to implement the aerosol and cloud based solar radiation short-term forecast model creation method and/or the solar radiation forecast method;
one or more input/output I/O interfaces 303, coupled between the processor 301 and the memory 302, are configured to enable information interaction of the processor 301 with the memory 302.
Wherein the processor 301 is a device having data processing capabilities, including but not limited to a Central Processing Unit (CPU) or the like; memory 302 is a device with data storage capability including, but not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically charged erasable programmable read-only memory (EEPROM), FLASH memory (FLASH); an I/O interface (read/write interface) 303 is connected between the processor 301 and the memory 202 to enable information interaction between the processor 301 and the memory 302, including but not limited to a data Bus (Bus) or the like.
In some embodiments, processor 301, memory 302, and I/O interface 303 are connected to each other and, in turn, to other components of the computing device via bus 304.
Embodiments of the present disclosure provide a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the aerosol and cloud-based solar radiation short-term forecast model creation method and/or the solar radiation forecast method.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components.
Some or all of the physical components may be implemented as software executed by a processor, such as a Central Processing Unit (CPU), digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, random access memory (RAM, more particularly SDRAM, DDR, etc.), read-only memory (ROM), electrically charged erasable programmable read-only memory (EEPROM), FLASH memory (FLASH), or other magnetic disk storage; a compact disk read-only (CD-ROM), digital Versatile Disk (DVD) or other optical disk storage; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage; any other medium that can be used to store the desired information and that can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The present disclosure has disclosed example embodiments, and although specific terms are employed, they are used and should be interpreted in a generic and descriptive sense only and not for purpose of limitation. In some instances, it will be apparent to one skilled in the art that features, characteristics, and/or elements described in connection with a particular embodiment may be used alone or in combination with other embodiments unless explicitly stated otherwise. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the disclosure as set forth in the appended claims.
Claims (12)
1. A method for creating a short-term solar radiation forecast model based on aerosol and cloud, the method comprising:
starting a Solar radiation forecast WRF-Solar numerical mode and a weather chemistry forecast WRF-Chem numerical mode to obtain a weather chemistry Solar radiation forecast WRF-Chem-Solar online coupling mode;
establishing a pollution source list of a WRF-Chem-Solar online coupling mode by adopting pollution source data comprising local atmospheric pollution emission source data and biological source emission data;
adopting a preset grid point statistical difference GSI assimilation system to assimilate data of the WRF-Chem-Solar online coupling mode after the pollution source list is created; the assimilation treatment comprises: weather data assimilation, aerosol data assimilation, and cloud data assimilation;
evaluating the WRF-Chem-Solar online coupling mode after data assimilation treatment;
and under the condition that the evaluation result meets the preset requirement, taking the WRF-Chem-Solar online coupling mode after the assimilation treatment parameters are stored as a Solar radiation short-term forecasting model based on aerosol and cloud.
2. The aerosol and cloud based short-term solar radiation forecast model creation method of claim 1, further comprising:
under the condition that the evaluation result does not meet the preset requirement, adjusting the assimilation treatment parameters of the WRF-Chem-Solar online coupling mode;
evaluating the WRF-Chem-Solar online coupling mode after parameter adjustment;
the assimilation treatment parameters comprise at least one of the following: background error covariance, observation error covariance, and assimilation time window; the background error covariance refers to the background field error covariance; the observation error covariance refers to an observation data error covariance, and the observation data includes: meteorological data, aerosol data, and cloud data.
3. The method for creating the short-term Solar radiation forecast model based on aerosol and cloud as recited in claim 1, wherein said creating a pollution source list of said WRF-Chem-Solar on-line coupling mode using pollution source data comprising local atmospheric pollution emission source data and biological source emission data comprises:
obtaining a local emission source list according to the local atmospheric pollution emission source data;
taking output data of a preset period input by a global atmospheric air conveying mode MOZART as a chemical field;
acquiring an online gas and aerosol emission model list MEGAN list from the natural world as a biological source emission list;
and taking the local emission source list, the chemical field and biological source emission list as a pollution source list of the WRF-Chem-Solar online coupling mode.
4. A method of creating an aerosol and cloud based short-term solar radiation prediction model as defined in claim 3, wherein said deriving a local emissions source list from said local atmospheric pollution emissions source data comprises:
updating a multi-scale emission list MEIC by using the local atmospheric pollution emission source data to obtain local emission source list data;
interpolating the local emissions source inventory data onto pattern grid points of the WRF-Chem-Solar online coupling pattern using a sparse matrix emissions inventory processing system SMOKE source pattern;
and obtaining the emission source intensity according to the local emission source list data, and carrying out gridding treatment on the emission source intensity to obtain the local emission source list.
5. The method for creating the short-term Solar radiation prediction model based on aerosol and cloud according to claim 1, wherein the step of performing data assimilation processing on the WRF-Chem-Solar online coupling mode after creating the pollution source list by using a preset grid point statistical difference GSI assimilation system comprises the following steps:
assimilating meteorological observation data based on the GSI assimilation system to realize the assimilation of meteorological data;
based on Gorad chemical aerosol radiation and transmission scheme GOCART, the GSI assimilation system is utilized to assimilate the observation data of the preset inhalable particles, so as to realize the aerosol data assimilation; the method comprises the steps of,
assimilating cloud data products of a preset satellite based on the GSI assimilation system to realize the cloud data assimilation; the cloud data product includes at least one of: cloud coverage, cloud top temperature, and cloud top air pressure.
6. The method for creating a short-term solar radiation forecast model based on aerosols and clouds according to claim 5, wherein said method for assimilating the aerosol data by assimilating the observation data of the preset inhalable particulate matter using a preset grid point statistical difference GSI assimilation system based on golddar chemical aerosol radiation and transmission scheme, comprises:
encoding the data of the inhalable particles into data in a preset format;
adding the data in the preset format into GDAS analysis data of a global data assimilation system;
and combining the added GDAS analysis data with ground station data to realize assimilation of the observation data of the preset inhalable particles.
7. The aerosol and cloud based short-term solar radiation forecast model creation method of claim 6, wherein said combining the added GDAS analysis data with the ground station data comprises: and performing at least one of the following processing on the added GDAS analysis data and the ground station data:
the binary universal format BUFR representing meteorological data is decoded, complemented, encoded, overall detected, quality controlled and offset corrected.
8. A method of solar radiation forecasting, the method comprising:
acquiring the aerosol-and-cloud-based solar radiation short-term prediction model created by the aerosol-and-cloud-based solar radiation short-term prediction model creation method according to any one of claims 1 to 7;
periodically carrying out assimilation treatment on the solar radiation short-term forecasting model based on aerosol and cloud according to observation data comprising meteorological data, aerosol data and cloud data;
and forecasting solar radiation by adopting an assimilation-treated solar radiation short-term forecasting model based on aerosol and cloud.
9. An aerosol and cloud-based solar radiation short-term forecast model creation apparatus, comprising:
the starting module is used for starting a Solar radiation forecast WRF-Solar numerical mode and a weather chemistry forecast WRF-Chem numerical mode to obtain a weather chemistry Solar radiation forecast WRF-Chem-Solar online coupling mode;
the establishing module is used for establishing a pollution source list of the WRF-Chem-Solar online coupling mode by adopting pollution source data comprising local atmospheric pollution emission source data and biological source emission data;
the first assimilation module is used for carrying out data assimilation treatment on the WRF-Chem-Solar online coupling mode after the pollution source list is created by adopting a preset grid point statistical difference GSI assimilation system; the assimilation treatment comprises: weather data assimilation, aerosol data assimilation, and cloud data assimilation;
the evaluation module is used for evaluating the WRF-Chem-Solar online coupling mode after the data assimilation treatment;
and the processing module is used for taking the WRF-Chem-Solar on-line coupling mode after the assimilation processing parameter is stored as a Solar radiation short-term forecasting model based on aerosol and cloud under the condition that the evaluation result meets the preset requirement.
10. A solar radiation forecasting device, comprising:
an acquisition module for acquiring the aerosol-and-cloud-based solar radiation short-term prediction model created by the aerosol-and-cloud-based solar radiation short-term prediction model creation method according to any one of claims 1 to 7;
the second assimilation module is used for carrying out periodic assimilation treatment on the solar radiation short-term forecast model based on the aerosol and the cloud according to observation data comprising meteorological data, aerosol data and cloud data;
and the forecasting module is used for forecasting solar radiation by adopting an assimilation-processed solar radiation short-term forecasting model based on aerosol and cloud.
11. An electronic device, the electronic device comprising:
one or more processors;
a memory having stored thereon one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the aerosol and cloud-based short-term solar radiation prediction model creation method of any of claims 1-7, and/or the solar radiation prediction method of claim 8;
one or more input/output I/O interfaces coupled between the processor and the memory configured to enable information interaction of the processor with the memory.
12. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the aerosol and cloud based solar radiation short-term prediction model creation method of any of claims 1-7 and/or the solar radiation prediction method of claim 8.
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